Search Results - (( java application customization algorithm ) OR ( features motion learning algorithm ))

Fish Motion Trajectories Detection Algorithm Based on Spiking Neural Network (S/O: 12893) by Yusoff, Nooraini, Yusof, Yuhanis, Siraj, Fadzilah, Ahmad, Farzana Kabir

“…The spike encoding was used for feature extraction. The algorithm for this learning model adopted the reward-modulated STDP. …”
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Study of machine learning in computer vision using Raspberry Pi by Chong, Kah Luo

Design and optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions by Ibrahimy, Muhammad Ibn, Ahsan, Md. Rezwanul, Khalifa, Othman Omran

“…Between the Levenberg-Marquardt and scaled conjugate gradient learning algorithms, the aforesaid algorithm shows better classification performance. …”
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Particle swarm optimization with deep learning for human action recognition by Usmani, U.A., Watada, J., Jaafar, J., Aziz, I.A., Roy, A.

“…This decreases the detection efficiency and degrades the target tracking output. Also, the current motion target detection algorithms extract features from the relevant object only if the moving object has complex texture features. …”
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Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy by Ganesh , Krishnasamy

“…The proposed algorithm is compared with the state-of-the-art feature selection algorithms using three different datasets. …”
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Hand gesture recognition in ASD related motion using YOLOv8 algorithm / Muhammad Afiq Mohd Ali by Mohd Ali, Muhammad Afiq

“…To enhance recognition accuracy, integration of other motion features that involved body posture and facial expressions should be completed. …”
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Multi-sensor fusion and deep learning framework for automatic human activity detection and health monitoring using motion sensor data / Henry Friday Nweke by Henry Friday , Nweke

“…This is further worsen by the use of single sensors modality and machine learning algorithms. Furthermore, developing robust and efficient methods are required to handle issues such as orientation and position displacement, sensor fusion and feature incompatibility, automatic feature representation, and how to minimize intra-class similarity and inter-class variability. …”
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Design and performance analysis of artificial neural network for hand motion detection from EMG signals by Ibrahimy, Muhammad Ibn, Ahsan, Md. Rezwanul, Khalifa, Othman Omran

“…The conventional and most effective time and timefrequency based features are extracted and normalized. The neural network has been trained with the normalized feature set with supervised learning method. …”
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Lightweight spatial attentive network for vehicular visual odometry estimation in urban environments by Gadipudi, N., Elamvazuthi, I., Lu, C.-K., Paramasivam, S., Su, S.

“…On the other hand, learning-based methods automatically learn the features required through motion mapping. …”
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Lightweight spatial attentive network for vehicular visual odometry estimation in urban environments by Gadipudi, N., Elamvazuthi, I., Lu, C.-K., Paramasivam, S., Su, S.

“…On the other hand, learning-based methods automatically learn the features required through motion mapping. …”
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Wearable Sensor Feature Fusion for Human Activity Recognition (HAR) : A Proposed Classification Framework by Norfadzlan, Yusup, Adnan Shahid, Khan, Izzatul Nabila, Sarbini, Nurul Zawiyah, Mohamad

“…Human Activity Recognition (HAR) focuses on detecting people's daily regular activities based on time-series recordings of their actions or motions. Due to the extensive feature engineering and human feature extraction required by traditional machine learning algorithms, they are time consuming to develop. …”
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A comparative study and simulation of object tracking algorithms by Ji, Yuanfa, Yin, Pan, Sun, Xiyan, Kamarul Hawari, Ghazali, Guo, Ning

“…The algorithms using convolution features and multi-features fusion algorithms have more advantages in tracking accuracy than the algorithm using a single feature, but the tracking speed will also drop rapidly. …”
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Imitation learning through self-exploration : from body-babbling to visuomotor association / Farhan Dawood by Dawood, Farhan

“…The results show that the imitation learning algorithm is able to incrementally learn and associate the observed motion patterns based on the segmentation of motion primitives.…”
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The classification of wink-based eeg signals by means of transfer learning models by Jothi Letchumy, Mahendra Kumar

“…Although motor imagery signals have been used in assisting the hand grasping motion amongst others motions, nonetheless, such signals are often difficult to be generated. …”
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Feature-based stereo vision relative positioning strategy for formation control of unmanned aerial vehicles by Yaghoobi, Yousef, Bahiki, Muhammad Rijaluddin, Syaril Azrad

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A Reinforcement Learning Based Adaptive ROI Generation for Video Object Segmentation by Usmani, U.A., Watada, J., Jaafar, J., Aziz, I.A., Roy, A.

“…The primary focus of the current deep learning-based models is to learn the discriminative representations in the foreground over motion and appearance in small-term temporal segments. …”
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AR oriented pose matching mechanism from motion capture data by Iqbal J., Sidhu M.S., Ariff M.B.M.

Fuzzy qualitative approach to address uncertainty in human motion analysis / Lim Chern Hong by Lim, Chern Hong

“…Human modelling is the enabling step in the human motion analysis system where the identified person from a video camera will be projected and represented in a better model to ease the latter processes such as feature extraction. …”
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Machine learning application in predicting anterior cruciate ligament injury among basketball players by Longfei, Guo

“…SHAP analysis identified key feature with the highest predictive value for ACL injury during specific sports motions. …”
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Development of electromyography-controlled 3D printed robot hand and supervised machine learning for signal classification by Abdul Wahit, Mohamad Aizat

“…In another hand, the movement accuracy analysis of the hand robot motion range was performed by comparing the expected motion range and the motion range achieved by 3D printed hand robot. …”
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